ArticleJournal of cheminformatics2013
The influence of the inactives subset generation on the performance of machine learning methods.
Article in Journal of cheminformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 40 citations in OpenAlex.
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- Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions.Journal of chemical information and modeling · 2020Article
- Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.Molecules (Basel, Switzerland) · 2019Article
- Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening.PloS one · 2019Article
- Unearthing new genomic markers of drug response by improved measurement of discriminative power.BMC medical genomics · 2018Article
- How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors.Frontiers in chemistry · 2018Article
- Predicting novel substrates for enzymes with minimal experimental effort with active learning.Metabolic engineering · 2017Article
- Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.Molecular diversity · 2017Article
- Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds.Journal of chemical information and modeling · 2017Article
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- An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity.PloS one · 2016Article
- Target prediction utilising negative bioactivity data covering large chemical space.Journal of cheminformatics · 2015Article
- The influence of negative training set size on machine learning-based virtual screening.Journal of cheminformatics · 2014Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years. However, their effectiveness is strongly dependent on many different factors.
resultsIn this study, the influence of the way of forming the set of inactives on the classification process was examined: random and diverse selection from the ZINC database, MDDR database and libraries generated according to the DUD methodology. All learning methods were tested in two modes: using one test set, the same for each method of inactive molecules generation and using test sets with inactives prepared in an analogous way as for training. The experiments were carried out for 5 different protein targets, 3 fingerprints for molecules representation and 7 classification algorithms with varying parameters. It appeared that the process of inactive set formation had a substantial impact on the machine learning methods performance.
conclusionsThe level of chemical space limitation determined the ability of tested classifiers to select potentially active molecules in virtual screening tasks, as for example DUDs (widely applied in docking experiments) did not provide proper selection of active molecules from databases with diverse structures. The study clearly showed that inactive compounds forming training set should be representative to the highest possible extent for libraries that undergo screening.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.